In embodied intelligence systems, the motion controller serves as the critical bridge between semantic reasoning and physical execution. While humanoid control has progressed rapidly through large-scale human motion-capture data and motion-tracking paradigms, producing scalable and physically feasible motion corpora for quadruped robots faces fundamental obstacles: animal motion data is scarce, and cross-embodiment retargeting remains fragile.
We present ABot-C0, a generalist motion-control system for quadruped robots that establishes three complementary behavior foundations: a scalable multi-source motion-data pipeline, robust policy learning across motion tracking, locomotion, and scene interaction, and a unified deployment stack for reliable real-world operation. Fundamentally, we construct a data pyramid through conditional video-generation synthesis, annotated motion capture, teleoperation, and human design, producing 16,074 physically feasible motion clips as the data foundation for various motion learning demands.
We then train a Flow-Matching generalist policy that demonstrates for the first time quadruped motion tracking scaling law, whereby its performance improves consistently as training scales up, with zero-shot capability to track unseen motions. We further advance robust all-terrain traversal locomotion by adopting a three-stage privileged-to-perceptive framework with temporal LiDAR memory and terrain-predictive supervision. Collectively, these components form a motion generalist that coordinates multi-policy execution, smooth behavior transitions, energy-efficient control, and safety mechanisms for real-world deployment.
Extensive experiments on urban-terrain autonomous navigation and companion-style multimodal interaction demonstrate that quadruped robots move beyond single-function demos toward product-level behavioral intelligence.
Blogger's Review: ABot-C0 offers an innovative framework that significantly enhances quadruped robots' motion capabilities through a rich data foundation and robust policy learning. This approach not only addresses the issue of data scarcity but also provides reliable solutions for real-world applications, showcasing extensive potential and practicality.